add1.cca {vegan} | R Documentation |
Compute all single terms that can be added or dropped from a constrained ordination model.
## S3 method for class 'cca': add1(object, scope, test = c("none", "permutation"), pstep = 100, perm.max = 200, ...) ## S3 method for class 'cca': drop1(object, scope, test = c("none", "permutation"), pstep = 100, perm.max = 200, ...)
object |
A constrained ordination object from
cca , rda or capscale . |
scope |
A formula giving the terms to be considered for adding
or dropping; see add1 for details. |
test |
Should a permutation test added using anova.cca . |
pstep |
Number of permutations in one step, passed as argument
step to anova.cca . |
perm.max |
Maximum number of permutation in anova.cca . |
... |
Other arguments passed to add1.default ,
drop1.default , and anova.cca . |
With argument test = "none"
the functions will only call
add1.default
or drop1.default
. With
argument test = "permutation"
the functions will add test
results from anova.cca
. Function drop1.cca
will
call anova.cca
with argument by = "margin"
.
Function add1.cca
will implement a test for single term
additions that is not directly available in anova.cca
.
Functions are used implicity in step
. The
deviance.cca
and deviance.rda
used in
step
have no firm basis, and setting argument
test = "permutation"
may help in getting useful insight into
validity of model building. Meticulous use of add1.cca
and
drop1.cca
will allow more judicious model building.
The default perm.max
is set to a low value, because
permutation tests can take a long time. It should be sufficient to
give a impression on the significances of the terms, but higher
values of perm.max
should be used if P values really
are important.
Returns a similar object as add1
and drop1
.
Jari Oksanen
add1
, drop1
and
anova.cca
for basic methods. You probably need these
functions with step
. Functions
deviance.cca
and extractAIC.cca
are used
to produce the other arguments than test results in the
output. Functions cca
, rda
and
capscale
produce result objects for these functions.
data(dune) data(dune.env) ## Automatic model building with permutation tests step(rda(dune ~ 1, dune.env), reformulate(names(dune.env)), test="perm") ## Manual model building ## -- define the maximal model for scope mbig <- rda(dune ~ ., dune.env) ## -- define an empty model to start with m0 <- rda(dune ~ 1, dune.env) ## -- manual selection and updating add1(m0, scope=formula(mbig), test="perm") m0 <- update(m0, . ~ . + Management) add1(m0, scope=formula(mbig), test="perm") m0 <- update(m0, . ~ . + Moisture) ## -- included variables still significant? drop1(m0, test="perm") add1(m0, scope=formula(mbig), test="perm")